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20242026
most citedFindings of the IWSLT 2024 Evaluation Campaign

5 citations · 8 across the 13 of their papers we have counts for

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cs.SD2026

CrossEdit: Cross-Modal Training Enables Rich Audio-Visual Editing

William Chen, Prem Seetharaman, Ke Chen +10

Multimedia editing requires generative models to interpret complex, compositional instructions and modify only the desired elements across modalities, a capability largely beyond e…

cs.SD2026

LACE: Layer-Wise Compression for Dynamic Frame Rate Codecs

Thanapat Trachu, Samuele Cornell, William Chen +1

Neural audio codecs are a key component in speech language modeling. However, their high frame rates lead to long sequence lengths, increasing computational costs. Dynamic frame ra…

cs.SD2026

MulTTiPop: A Multitrack Transcription Dataset for Pop Music

Nathan Pruyne, Benjamin Stoler, William Chen +3

We present MulTTiPop, a dataset of pop music segments and their associated multitrack MIDI recordings for the evaluation of automatic music transcription models. MulTTiPop contains…

cs.SD2026

AudioChat: Unified Audio Storytelling, Editing, and Understanding with Transfusion Forcing

William Chen, Prem Seetharaman, Rithesh Kumar +4

Despite recent breakthroughs, audio foundation models struggle in processing complex multi-source acoustic scenes. We refer to this challenging domain as audio stories, which can h…

cs.SD2025

PURE Codec: Progressive Unfolding of Residual Entropy for Speech Codec Learning

Jiatong Shi, Haoran Wang, William Chen +4

Neural speech codecs have achieved strong performance in low-bitrate compression, but residual vector quantization (RVQ) often suffers from unstable training and ineffective decomp…